We analyze group distributional robustness of BC, were we consider model updates under data shifts. We assume data samples come from a mixture of distributions corresponding to different groups (e.g., domains, demographics, classes); as new samples are acquired, the overall group composition of the training set may drift over time. We wish to update our model over the newly augmented dataset in a way that guarantees a desired BC measure across all groups. We evaluate how existing ideas from group robust approaches [1]}, [2]}, [3]}, [4]}, [5]} can help in this scenario. We additionally propose two methods, Sample-Referenced Model (SRM) and Group-Referenced Model (GRM) to specifically tackle backward-compatible model updating. We use three common image classification datasets, CelebA, Waterbird, and CIFAR10 [6]}, [2]}, [8]}, [9]} for empirical validation.
